Download presentation

Presentation is loading. Please wait.

Published byAidan Garrow Modified over 2 years ago

1
Quantifying the changes in land use in developing countries using remote sensing: challenges and solutions Alfred Stein, Gao Wenxiu, Salma Anwar Alfred Stein, a.stein@utwente.nl Salma Anwar, anwar19631@itc.nl

2
This presentation Developing countries have specific problems Data availability can be poor, the areas are big but sometimes inaccessible There is much to be gained from earth observation satellites Problems can be specific Solutions can be drawn from spatial statistics 15 Nov12

3
Le menu du jour Use of modern techniques leads to novel ways of mapping Differences with existing methods can be big Automatic procedures may lead to odd situations that have to be resolved Spatial statistics may lead to tools and methods that can be of use to improve automation There is the common story: 15 Nov12

4
The common story 15 Nov12 Mathematics Statistics Problem Data Solution Reporting

5
Premier plat Landuse change in china 15 Nov 2012

6
Land use change in China In China there are different classification systems for land use There is land owned by many owners A main concern is the updating of existing maps Classifications may have changed: object oriented classification in stead of pixel based classification Increasingly satellite images are used for the purpose 15 Nov12

7
Land use in China 15 Nov12

8
Two Landuse Maps A traditional land-use map An image-derived land-use map 15 Nov12

9
Improving Representation of Land-use Maps Derived from Object-oriented Image Classification Intention: derive the vector landuse map from image with OO image Problems: For individual polygons: small, congested and twisted polygons exist with step-like boundaries. For a group of polygons: geometric conflicts between polygons (e.g. unreadable small areas and narrow corridors) Unclassified polygons Methodology: Map generalization combining with a polygon similarity model and spectral information from images. 15 Nov12

10
Problems in an OO image-derived Landuse Map (1) Individual polygons: Congested polygons Twisted polygons Narrow corridors Step-like boundaries. 15 Nov12

11
Problems in OO image-derived Landuse Map (2) A group of polygons: Geometric conflicts Unreadable small areas Narrow corridors 15 Nov12

12
Methodology A framework for improving representation of OO image-derived land-use maps. Polygon similarity model Outward-inward-buffering Elimination of small polygons 15 Nov12

13
The Framework Resolve problematic polygons Final land-use map Manipulate unclassified polygons Original image-derived land-use map Resolve geometric conflicts - Eliminate small polygons - Resolve narrow-corridor conflicts - Smoothen boundaries of polygons Evaluate optimized output Preliminary optimized output Detect problematic polygons 15 Nov 12

14
Spectral similarity Spectral similarity (SP) quantifies the degree of resemblance in spectral characteristics of P i and P k and is calculated as the difference between their spectral values. The spectral values are described as the standard deviation of DN values the pixels covered by a polygon (brightness). Brightness contains the spectral characteristics of different layers of the image. A lower SP value corresponds with more similar spectral characteristics of two polygons. 13Nov12

15
Semantic similarity Semantic similarity (SE) measures the equivalence in land-use of P i and P k It is determined by the relationship between land-use classes of P i and P k based on a hierarchical land-use classification system: n: nr of class levels in the land-use classification system. V l = 1 if P i and P k belong to the same land-use class at the lth level, and 0 otherwise. If V l = 1 and l > 1, then V 1 =…= V l-1 = V l =1 and V l+1 = …=V n =0. A larger SE value corresponds with a closer semantic relationship. 13Nov12

16
Semantic similarity: some cases The land-use classes of P i and P k are identical at l = 3, e.g. the both polygons belong to Class I. Then V 1 = V 2 = V 3 = 1, and thus SE = 2. The land-use classes of P i and P k are different at l = 3, e.g. P i belongs to Class 1 and P k belongs to Class 2, but they belong to the same class A at Level 2. Then V 1 = V 2 = 1, V 3 = 0, and thus SE = 1. The land-use classes of P i and P k differ at Levels 2 and 3, e.g. P i belongs to Class A and P k belongs to Class B, but they belong to the same class (e.g. Class II) at Level 1. Then V 1 = 1 and V 2 =V 3 = 0, and thus SE = 1/3. The land-use classes of P i and P k are different at all levels, e.g. P i belongs to Class I and P k one belongs to Class X. Then SE = 0. 13Nov12

17
Geometric similarity Geometric similarity (GE) measures the resemblance in shape (size, perimeter) characteristics SI i of P i and SI k of P k. For eliminating a small polygon P i, GE equals the ratio of the length of the sharing boundaries P i with its neighbor polygon P k to its perimeter. This shape index quantifies the difference in shape between a polygon and the circle with the same area. The small polygon is merged with its neighbor with the largest GE value. Thus the possibility is eliminated of introducing new narrow-corridor conflicts when eliminating the small polygon. For unclassified polygons, GE adopts the difference in the shape index of two polygons as 13Nov12

18
Polygon similarity model Polygon similarity (S) is defined as the degree of similarity of two polygons depending on their contextual characteristics. Spectral characteristics (SP) Semantic characteristics (SE) geometric characteristics (GE) 13Nov12

19
Eliminate small polygons Basic solution: merged with the neighbor with the highest polygon similarity 13Nov12

20
Outward-inward-buffering To resolve narrow-corridor conflicts existing in polygons. Basic rationale: an outward-buffering process (dilation process) + an inward-buffering process (erosion process) 13Nov12

21
Improved Map image-derived land-use map at 1:10000 image-derived land-use map at 1:50000 13Nov12

22
We notice… Well developed spatial statistical techniques are able to resolve emerging problems in new classification procedures Further optimization is to be done Automating updating steps is receiving a new flavor There is room for a further (probabilistic) approach 13Nov12

23
Seconde plat Deforestation in the Amazonian 15 Nov 2012

25
Selective Logging In the Brazilian Amazonia, selective logging is a major source of forest degradation Detection and analysis of selective logging is an important challenge to forest researchers Log-landing sites serve as proxy for selective logging activities Spatial point pattern statistics may serve as an important tool for analyzing patterns of log-landing sites 13Nov12

26
Selective Logging Detection 13Nov12

27
Study area 13Nov12

28
Map of log-landings (2000) 650 locations 13Nov12

29
Point pattern statistics First order characteristics where dx is a small region located at x of the log-landing pattern X, |dx| being its area and N(dx) is the number of log-landings in dx Second order characteristics 13Nov12

30
Distance summary functions Nearest neighbor distance distribution function Empty space distance distribution function The J-function 13Nov12

31
Stationarity: all properties of a pattern remain invariant under translation (constant density) Non-stationarity: configuration of the pattern depends on the locations (variable density) variability due to environmental heterogeneity interactions between the points In case of non-stationarity: Markov Chain Monte Carlo methods (MCMC) become computationally extensive Stationarity vd. Non-stationarity 13Nov12

32
Estimation of the intensity function and choice of the kernel bandwidth Intensity function is generally unknown and estimated non- parametrically using kernel smoothing Suitable choice of kernel bandwidth is the main challenge in estimation of the intensity function 13Nov12

33
kernel size=10kernel size=30kernel size=40kernel size=50 Kernel density estimate with kernel size=10 km 13Nov12

34
Kernel density estimate with kernel size=20 km 13Nov12

35
Kernel density estimate with kernel size=30 km 13Nov12

36
Kernel density estimate with kernel size=40 km 13Nov12

37
Kernel density estimate with kernel size=50 km 13Nov12

38
Observations A larger value of kernel bandwidth r reduces the interaction distance between the log-landing sites, thus reducing the effective range of interaction distance r over which the J-function is calculated. As the value of r increases beyond its effective range, the simulated envelopes span over wider range and relative noise in the simulated envelopes also increases. Relative noise in the calculated J-function also increases beyond the effective range of r 13Nov12

39
Map of loglandings (2001) 917 locations 13Nov12

40
kernel size=10kernel size=20kernel size=30kernel size=40 Kernel density estimate with kernel size=20 km 13Nov12

41
Kernel density estimate with kernel size=30 km 13Nov12

42
Kernel density estimate with kernel size=40 km 13Nov12

43
To summarize The presented visual and graphical methods provide a useful tool to get an insight into the spatial characteristics of log-landings distribution. Spatial statistics was useful for analysis and interpretation of the pattern of log-landing sites. The inhomogeneous J-functions helps to infer the type and ranges of interaction using non-parametric form of intensity. The selective logging operations are strongly aggregated with in the study area The appropriates bandwidth increased from 20 to 30 km within a single year, indicating an increase in the extent of the clustering of log-landing sites. 13Nov12

44
Further work Fitting a spatial point pattern model to explain the clustered pattern of log- landing sites in terms of related environmental and geographic factors 13Nov12

45
Le desert A new scientific journal 15 Nov 2012

46
A new journal ees.elsevier.com\spasta 13Nov12

47
The history First ideas date back from 2007 Aims and scope were defined A key word analysis was done 2007 – 2010: discussing it @ Elsevier Reluctance because of the economic crisis Reluctance because of increasing e-journals and internet There was a recent journal in a related area: Spatial and Spatio-Temporal Epidemiology, Andrew Lawson editor in chief No new journals 13Nov12

48
Then, in 2010… We had the idea for a conference to check the support Elsevier organized the meeting Conference took place in Enschede, in 2011 It was a great success (> 300 participants) This convinced Elsevier that it was a good idea to continue I was formally invited to become the ed-in-chief The first issue appeared in 2012, containing a wide range of publications The second issue is in press 13Nov12

49
Aims and scope (1) The aim of the journal is to be the leading journal in the field of spatial statistics. It publishes articles at the highest scientific level concerning important and timely developments in the theory and applications of spatial and spatio-temporal statistics. It favors manuscripts that present theory generated by new applications, or where new theory is applied to an important spatial problem. A purely theoretical study will only rarely be acceptable without a proper application, whereas a single case study is not acceptable for publication. 13Nov12

50
Aims and scope (2) Spatial statistics concerns the quantitative analysis of spatial data, including their dependencies and uncertainties. The extension to spatio-temporal statistics includes the time dimension as well. The three major groups of data are covered: lattice data that are collected on a predefined lattice geostatistical data that represent continuous spatial variation spatial point data that are observed at random locations. These types of data have their logical extension into the space-time domain, where the relations remain similar, but estimation may be different. 13Nov12

51
Aims and scope (3) Methodology for spatial statistics is found in probability, stochastics and mathematical statistics as well as in information science. Typical applications are mapping of the data, issues of spatial data quality, modeling the dependency structure and drawing valid inference on the basis of a limited set of data. Applications of spatial statistics occur in a broad range of disciplines: agriculture, geology, soils, hydrology, the environment, ecology, mining, oceanography, forestry, air quality, remote sensing, but also in social/economic fields like spatial econometrics, epidemiology and disease mapping. 13Nov12

52
The future (4) We are looking for good papers! To report your science To communicate your findings To have feedback from colleagues 13Nov12

53
The end 13Nov12

Similar presentations

Presentation is loading. Please wait....

OK

By: Asma Al-Oneazi Supervised by… Dr. Amal Fatani.

By: Asma Al-Oneazi Supervised by… Dr. Amal Fatani.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google